Gamma distribution based predicting model for breast cancer drug response based on multi-layer feature selection

Frontiers in Genetics

Genetics

Jia Wang¹  Tongtong Cui²  Hong Gu²  Pan Qin²  Zeyuan Wang² 

[1] Department of Breast Surgery, Second Hospital of Dalian Medical University, Dalian, Liaoning, China;Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China;

关键词: drug response;    machine learning;    feature selection;    breast cancer;    generalized linear model;    artificial neural network;   

DOI  :  10.3389/fgene.2023.1095976

 received in 2022-11-11, accepted in 2023-01-23,  发布年份 2023

来源: Frontiers

【 摘 要 】

In the pursuit of precision medicine for cancer, a promising step is to predict drug response based on data mining, which can provide clinical decision support for cancer patients. Although some machine learning methods for predicting drug response from genomic data already exist, most of them focus on point prediction, which cannot reveal the distribution of predicted results. In this paper, we propose a three-layer feature selection combined with a gamma distribution based GLM and a two-layer feature selection combined with an ANN. The two regression methods are applied to the Encyclopedia of Cancer Cell Lines (CCLE) and the Cancer Drug Sensitivity Genomics (GDSC) datasets. Using ten-fold cross-validation, our methods achieve higher accuracy on anticancer drug response prediction compared to existing methods, with an R2 and RMSE of 0.87 and 0.53, respectively. Through data validation, the significance of assessing the reliability of predictions by predicting confidence intervals and its role in personalized medicine are illustrated. The correlation analysis of the genes selected from the three layers of features also shows the effectiveness of our proposed methods.

【 授权许可】

Unknown   
Copyright © 2023 Cui, Wang, Gu, Qin and Wang.